Previous works on book success prediction have focused on extracting count-based, lexical and syntactic hand-crafted features and used these features for classification [1]}, [2]}. However, the quality of such methods heavily depends on the quality of the features extracted. In addition, while such features may represent the writing style of a given book, they fail to capture semantics, emotions, and plots. [3]} focused on modeling the emotion flow throughout the book arguing that book success relies mainly on the flow of emotions a reader feels while reading. However, emotions are only one aspect of the reading experience and while emotion flow may be an important element in fiction books, it is not the case for non-fiction ones. Our work proposes a method for book success prediction that requires no feature engineering and that takes into account the writing style, content, and semantics.
